arXiv 2304.091422023-04astro-ph.COastro-ph.IM

利用机器学习基于星系团性质开展宇宙学研究

Cosmology with Galaxy Cluster Properties using Machine Learning

Lanlan Qiu, Nicola R. Napolitano, Stefano Borgani, Fucheng Zhong, Xiaodong Li, Mario Radovich, Weipeng Lin, Klaus Dolag, Crescenzo Tortora, Yang Wang, Rhea-Silv… 展开作者

Lanlan Qiu, Nicola R. Napolitano, Stefano Borgani, Fucheng Zhong, Xiaodong Li, Mario Radovich, Weipeng Lin, Klaus Dolag, Crescenzo Tortora, Yang Wang, Rhea-Silvia Remus, Sirui Wu, Giuseppe Longo

备注：: 19 pages, 20 figures. Revised version after the referee report. Resubmitted to A&A
发表信息：: A&A 687, A1 (2024)
DOI：: 10.1051/0004-6361/202346683

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AI总结：

本研究提出首个利用星系团性质的机器学习方法，基于Magneticum模拟的模拟星表训练，实现对多个宇宙学参数的无偏约束，未来可应用于多波段巡天数据。

摘要：

星系团是宇宙中质量最大的引力束缚系统，被广泛认为是一种有效的宇宙学探针。我们提出了首个利用星系团性质对一组宇宙学参数（包括Omega_m、sigma_8、Omega_b和h_0）得出无偏约束的机器学习方法。我们使用包含Magneticum多宇宙流体动力学模拟“测量”量的模拟星表训练机器学习模型，这些测量量包括气体质量、气体热光度、气体温度、恒星质量、星系团半径、总质量、速度弥散和红移，并能正确预测所有参数，其不确定度量级约为：Omega_m ~14%、sigma_8 ~8%、Omega_b ~6%、h_0 ~3%。这一首次测试结果极具前景，它表明机器学习可以高效地将观测量多维空间中的相关性映射到宇宙学参数空间，并缩小给定样本属于某一特定宇宙学参数组合的概率。未来，这些机器学习工具可应用于拥有多波段观测的星系团样本，这些观测来自光学和近红外波段的LSST、CSST、Euclid、Roman等巡天项目，以及X射线波段的eROSITA，以同时约束宇宙学模型和重子反馈的效应。

英文摘要：

[Abridged] Galaxy clusters are the most massive gravitationally-bound systems in the universe and are widely considered to be an effective cosmological probe. We propose the first Machine Learning method using galaxy cluster properties to derive unbiased constraints on a set of cosmological parameters, including Omega_m, sigma_8, Omega_b, and h_0. We train the machine learning model with mock catalogs including "measured" quantities from Magneticum multi-cosmology hydrodynamical simulations, like gas mass, gas bolometric luminosity, gas temperature, stellar mass, cluster radius, total mass, velocity dispersion, and redshift, and correctly predict all parameters with uncertainties of the order of ~14% for Omega_m, ~8% for sigma_8, ~6% for Omega_b, and ~3% for h_0. This first test is exceptionally promising, as it shows that machine learning can efficiently map the correlations in the multi-dimensional space of the observed quantities to the cosmological parameter space and narrow down the probability that a given sample belongs to a given cosmological parameter combination. In the future, these ML tools can be applied to cluster samples with multi-wavelength observations from surveys like LSST, CSST, Euclid, Roman in optical and near-infrared bands, and eROSITA in X-rays, to constrain both the cosmology and the effect of the baryonic feedback.

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